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Conformal Prediction

Conformal Prediction is a machine learning framework that provides valid measures of confidence for individual predictions. It offers a principled approach to quantify uncertainty in predictions without assuming any specific distribution for the data. This section features papers that explore various aspects of conformal prediction, including theoretical advancements, algorithmic developments, and applications across different domains.

Papers

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Showing 221–230 of 704 papers

TitleStatusHype
Conformal Robust Beamforming via Generative Channel Models—0
Confidence Calibration for Systems with Cascaded Predictive Modules—0
Label Noise Robustness of Conformal Prediction—0
Assurance Monitoring of Learning Enabled Cyber-Physical Systems Using Inductive Conformal Prediction based on Distance Learning—0
Conformal Prediction on Quantifying Uncertainty of Dynamic Systems—0
Conformal Prediction Regions are Imprecise Highest Density Regions—0
CONFIDERAI: a novel CONFormal Interpretable-by-Design score function for Explainable and Reliable Artificial Intelligence—0
Conformal Safety Shielding for Imperfect-Perception Agents—0
CONFINE: Conformal Prediction for Interpretable Neural Networks—0
Copula-based conformal prediction for Multi-Target Regression—0
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